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Posted to issues@spark.apache.org by "Ryan Blue (JIRA)" <ji...@apache.org> on 2018/08/13 16:20:00 UTC
[jira] [Resolved] (SPARK-25060) PySpark UDF in case statement is
always run
[ https://issues.apache.org/jira/browse/SPARK-25060?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ryan Blue resolved SPARK-25060.
-------------------------------
Resolution: Won't Fix
I'm closing this issue as "Won't Fix", the same as the issue this duplicates, SPARK-22347.
> PySpark UDF in case statement is always run
> -------------------------------------------
>
> Key: SPARK-25060
> URL: https://issues.apache.org/jira/browse/SPARK-25060
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.1
> Reporter: Ryan Blue
> Priority: Major
>
> When evaluating a case statement with a python UDF, Spark will always run the UDF even if the case doesn't use the branch with the UDF call. Here's a repro case:
> {code:lang=python}
> from pyspark.sql.types import StringType
> def fail_if_x(s):
> assert s != 'x'
> return s
> spark.udf.register("fail_if_x", fail_if_x, StringType())
> df = spark.createDataFrame([(1, 'x'), (2, 'y')], ['id', 'str'])
> df.registerTempTable("data")
> spark.sql("select id, case when str <> 'x' then fail_if_x(str) else null end from data").show()
> {code}
> This produces the following error:
> {code}
> Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
> File "/mnt3/yarn/usercache/rblue/appcache/application_1533057049763_100912/container_1533057049763_100912_01_000002/pyspark.zip/pyspark/worker.py", line 189, in main
> process()
> File "/mnt3/yarn/usercache/rblue/appcache/application_1533057049763_100912/container_1533057049763_100912_01_000002/pyspark.zip/pyspark/worker.py", line 184, in process
> serializer.dump_stream(func(split_index, iterator), outfile)
> File "/mnt3/yarn/usercache/rblue/appcache/application_1533057049763_100912/container_1533057049763_100912_01_000002/pyspark.zip/pyspark/worker.py", line 104, in <lambda>
> func = lambda _, it: map(mapper, it)
> File "<string>", line 1, in <lambda>
> File "/mnt3/yarn/usercache/rblue/appcache/application_1533057049763_100912/container_1533057049763_100912_01_000002/pyspark.zip/pyspark/worker.py", line 71, in <lambda>
> return lambda *a: f(*a)
> File "<ipython-input-1-91ba29d7e46f>", line 4, in fail_if_x
> AssertionError
> {code}
> This is because Python UDFs are extracted from expressions and run in the BatchEvalPython node inserted as the child of the expression node:
> {code}
> == Physical Plan ==
> CollectLimit 21
> +- *Project [id#0L, CASE WHEN NOT (str#1 = x) THEN pythonUDF0#14 ELSE null END AS CASE WHEN (NOT (str = x)) THEN fail_if_x(str) ELSE CAST(NULL AS STRING) END#6]
> +- BatchEvalPython [fail_if_x(str#1)], [id#0L, str#1, pythonUDF0#14]
> +- Scan ExistingRDD[id#0L,str#1]
> {code}
> This doesn't affect correctness, but the behavior doesn't match the Scala API where case can be used to avoid passing data that will cause a UDF to fail into the UDF.
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